Real time analytics pertain to analyzing data as soon as the data enters a computer system. Enterprises have progressively developed various uses for real time analytics in order to perform tasks ranging from detecting trends in online user activity to optimizing daily activities. One form of real time analytics is downloadable reports that give insight on how organizations and individuals utilize applications and services at an administrative and user level. Historically, there has been a significant time delay between when the online activity occurs and when reports that reflect that online activity can be generated, resulting in decreased utility for the entire system, an increase in user dissatisfaction, and possibly errors and inconsistencies in the data reflected in the reports.
There have also been limitations to the accuracy of these reports due to issues such as data lost from online/offline workflow errors, lack of scalability, and inability to correct errors identified in the data. Lack of accuracy within these reports is counterproductive to the initiative to use data analytics for predicting the trends and behavior of network users, and the current process of checking the accuracy of these reports requires a significant amount of manual labor. Thus, there is a need for an implementation of near real time analytics that supports swift activity-to-analysis performance, data inconsistency correction, and efficient report generation.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.